Project description:The precise identification of Human Leukocyte Antigen class I (HLA-I) binding motifs plays a central role in our ability to understand and predict (neo-)antigen presentation in infectious diseases and cancer. Here, by exploiting co-occurrence of HLA-I alleles across publicly available as well as ten newly generated high quality HLA peptidomics datasets, we show that we can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. This fully unsupervised approach uncovers new motifs for several alleles without known ligands and significantly improves neo-epitope predictions in three melanoma patients.
Project description:HLA-DRB1 alleles have been associated with several autoimmune diseases. In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), HLA-DRB1 shared epitope (SE) alleles are the major genetic risk factors. In order to investigate whether expression of different alleles of major histocompatibility complex (MHC) Class II genes influence functions of immune cells, we investigated transcriptomic profiles of a variety of immune cells from healthy individuals carrying different HLA-DRB1 alleles. Sequencing libraries from peripheral blood mononuclear cells, CD4+ T cells, CD8+ T cells, and CD14+ monocytes of 32 genetically pre-selected healthy female individuals were generated, sequenced and reads were aligned to the standard reference. For the MHC region, reads were mapped to available MHC reference haplotypes and AltHapAlignR was used to estimate gene expression. Using this method, HLA-DRB and HLA-DQ were found to be differentially expressed in different immune cells of healthy individuals as well as in whole blood samples of RA patients carrying HLA-DRB1 SE-positive versus SE-negative alleles. In contrast, no genes outside the MHC region were differentially expressed between individuals carrying HLA-DRB1 SE-positive and SE-negative alleles. Existing methods for HLA-DR allele-specific protein expression were evaluated but were not mature enough to provide appropriate complementary information at the protein level. Altogether, our findings suggest that immune effects associated with different allelic forms of HLA-DR and HLA-DQ may be associated not only with differences in the structure of these proteins, but also with differences in their expression levels.
2020-12-22 | GSE163605 | GEO
Project description:Nanopore Sequencing of HLA alleles
Project description:HLA-I molecules bind short peptides and present them to CD8+ T cells for TCR recognition. The length of HLA-I ligands typically ranges from 8 to 12 amino acids, but high variability is observed between different alleles. Here we used recent HLA peptidomics data to analyze in an unbiased way peptide length distributions over 85 different HLA-I alleles. Our results revealed clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to unravel some of the molecular basis of peptide length distributions and predict peptide length distributions based on HLA-I sequences only. We further took advantage of our collection of curated HLA peptidomics studies to investigate multiple specificity in HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distributions and multiple specificity significantly improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on ten newly generated HLA peptidomics datasets from meningioma samples.